R Notebook

library(dplyr)
library(readr)
library(tidyr)
library(stringr)
library(ggplot2)
library(wesanderson)
library(RColorBrewer)
library(flextable)

library(TwoSampleMR)
ao <- available_outcomes()

rm(dat)

object 'dat' not found

dat <- read_tsv("../query_results/bc_all_mr.tsv") %>% 
  mutate(outcome = case_when(
        outcome.id %in% c('ieu-a-1127','ieu-a-1132', 'ieu-a-1133', 'ieu-a-1134') ~ 'ER+ postmeno',
        outcome.id %in% c('ieu-a-1128','ieu-a-1135', 'ieu-a-1136', 'ieu-a-1137') ~ 'ER- premeno',
        outcome.id %in% c('ieu-a-1126','ieu-a-1129', 'ieu-a-1130', 'ieu-a-1131') ~ 'Breast cancer (all)',
        outcome.id %in% c('ukb-a-519','ukb-a-55') ~ 'ER+ postmeno UKB')) %>% 
  mutate(chip = case_when(
        outcome.id %in% c('ieu-a-1126','ieu-a-1127', 'ieu-a-1128') ~ 'Meta',
        outcome.id %in% c('ieu-a-1129', 'ieu-a-1132','ieu-a-1135') ~ 'OncArr',
        outcome.id %in% c('ieu-a-1130', 'ieu-a-1133','ieu-a-1136') ~ 'iCOGS',
        outcome.id %in% c('ieu-a-1131', 'ieu-a-1134','ieu-a-1137') ~ 'Old',
        outcome.id %in% c('ukb-a-519','ukb-a-55') ~                  'UKBB')) %>% 
  mutate(outcome.details =  paste0(outcome.id, " (",format(outcome.sample_size, big.mark = ","))) %>% 
  separate(outcome.details, into=c('outcome.details', 'tmp'), sep=",") %>% 
  mutate(outcome.details = gsub("ieu-a-|ukb-a-", '', outcome.details),
         outcome.details = paste0(chip, ": ",outcome.details, "K)"))

dat<- dat %>% 
  mutate(outcome.details = factor(outcome.details, levels= c( 
                    sort(unique(dat$outcome.details)[grepl('Meta', unique(dat$outcome.details))]),
                    sort(unique(dat$outcome.details)[grepl('OncArr', unique(dat$outcome.details))]),
                    sort(unique(dat$outcome.details)[grepl('iCOGS', unique(dat$outcome.details))]),
                    sort(unique(dat$outcome.details)[grepl('Old', unique(dat$outcome.details))]),
                    sort(unique(dat$outcome.details)[grepl('UKBB', unique(dat$outcome.details))])  ))) %>% 
  # convert MR results to OR
  mutate(loci = mr.b - 1.96 * mr.se, 
         upci = mr.b + 1.96 * mr.se,
         or = exp(mr.b), 
         or_loci = exp(loci), 
         or_upci = exp(upci),
         OR_CI = paste0(round(or,2), " [",round(or_loci,2) ,":",round(or_upci,2), "]")) %>% 
  mutate(effect_direction = ifelse(or_loci > 1 & or_upci > 1, 'positive',
                            ifelse(or_loci < 1 & or_upci < 1, 'negative', 'overlaps null'))) %>% 
  mutate(mr.b = round(mr.b, 3)) %>% 
  # subset 
  filter(exposure.sex != 'Males') %>% 
  filter(chip!='old') %>% 
  #filter(mr.pval < 10e-6) %>% 
  # create categories of exposure traits
      # tidy up labels
   mutate(exposure = str_replace(exposure.trait, "Treatment/medication code", "Drug"),
          exposure = str_replace(exposure, "Non-cancer illness code  self-reported", "Illness"),
          exposure = str_replace(exposure, "Diagnoses - main ICD10:", "Diagnosis"),
          exposure = str_replace(exposure, "Mineral and other dietary supplements:", "Supplements"),
          exposure = str_replace(exposure, "Vitamin and mineral supplements:", "Vitamins"),
          exposure = str_replace(exposure, "Illness  injury  bereavement  stress in last 2 years", "Stress"),
          exposure = str_replace(exposure, "Types of transport used (excluding work)", "Transport"),
          exposure = str_replace(exposure, "Types of physical activity in last 4 weeks", "Physical activity")) %>% 
  mutate(exposure = case_when(grepl("presbyopia", exposure) ~ "Eyesight problems1",
                              grepl("hypermetropia", exposure) ~ "Eyesight problems2",
                              TRUE ~ exposure)) %>% 
  mutate(exposure_cat = case_when(
          grepl("Drug", exposure) ~ "drugs",
          grepl("Diagnos|Illness|cancer|diagnos|death|disorder|eye|carcino|colitis|disease", exposure, ignore.case = T) ~ "disease",
          grepl("vitamin|suppl", exposure, ignore.case = T) ~ "vitamin",
          grepl("waist|hip|obesity|trunk|mass|weight|bmi|body size|height|length|impedance", exposure, ignore.case = T) ~ "antrophometric",
          grepl("age at|age started", exposure, ignore.case = T) ~ "reproductive",
          grepl("alco|wine", exposure, ignore.case = T) ~ "alcohol",
          grepl("smok|cigar", exposure, ignore.case = T) ~ "smoking",
          grepl("activi|transport|diy", exposure, ignore.case = T) ~ "activity",
          TRUE ~ 'other')) 
  
# add notes for trats
exposure_notes <- ao %>% filter(id %in% dat$exposure.id) %>% select(id, note)

dat<- dat %>% 
  left_join(exposure_notes, by = c('exposure.id'='id')) %>% 
  # add other exposure details
  mutate(exposure = case_when(
    exposure.sex == 'Females' ~ paste0(exposure, " (F) ", coalesce(consortium, author),"/" , year),
                         TRUE ~ paste0(exposure, " (M/F) ", coalesce(consortium, author),"/" , year))) %>% 
  # add note
  mutate(exposure = case_when(grepl('Adjusted for BMI', note) ~ paste0(exposure, " AdjBMI"),
                        TRUE ~ exposure)) 

outcome_table<- dat %>% 
  select(outcome, outcome.id, outcome.sample_size, N_case, chip)%>% 
  distinct() %>%
  arrange(outcome, desc(outcome.sample_size)) %>% 
  rename(`sample_size`=outcome.sample_size)


set_flextable_defaults(big.mark = " ", 
  font.size = 10, theme_fun = theme_vanilla,
  padding.bottom = 6, 
  padding.top = 6,
  padding.left = 6,
  padding.right = 6,
  background.color = "#EFEFEF")

ft<-flextable(outcome_table)
ft<-width(ft, j = 1, width=1.5)
ft

NA
NA

Anthophometric traits

input <- dat %>% filter(exposure_cat %in% c('antrophometric')) %>% 
   mutate(exposure = gsub("Comparative ", "", exposure)) 

p<-plot_heatmap(input)
p

input <- dat %>% filter(exposure_cat %in% c('antrophometric')) %>% 
   mutate(exposure = gsub("Comparative ", "", exposure)) 
p<-plot_bubble_plot(input)
p

Activity

input <- dat %>% filter(exposure_cat %in% c('activity')) %>% 
                mutate(exposure = gsub("\\(eg.*)", "", exposure))
p<-plot_heatmap(input)
p

input <- dat %>% filter(exposure_cat %in% c('activity')) %>% 
              mutate(exposure = gsub("\\(eg.*)", "", exposure))
p<-plot_bubble_plot(input)
p

Supplements / vitamins

input <- dat %>% filter(exposure_cat %in% c('vitamin')) 
p<-plot_heatmap(input)
p

input <- dat %>% filter(exposure_cat %in% c('vitamin')) 
p<-plot_bubble_plot(input)
p

Reproductive traits

input <- dat %>% filter(exposure_cat %in% c('reproductive')) %>% 
                mutate(exposure = gsub("\\(eg.*)", "", exposure))
p<-plot_heatmap(input)
p

input <- dat %>% filter(exposure_cat %in%  c('reproductive')) %>% 
                mutate(exposure = gsub("\\(eg.*)", "", exposure))
p<-plot_bubble_plot(input)
p

ply<-plotly::ggplotly(p)
ply

Other

input <- dat %>% filter(exposure_cat %in% c('other')) %>% 
                filter(!grepl("inhaler", exposure))
p<-plot_heatmap(input)
p

input <- dat %>% filter(exposure_cat %in% c('other')) %>% 
                filter(!grepl("inhaler", exposure))
p<-plot_bubble_plot(input)
p

Drugs

input <- dat %>% filter(exposure_cat %in% c('drugs')) 
p<-plot_bubble_plot(input)
p

Disease

input <- dat %>% filter(exposure_cat %in% c('disease')) 
p<-plot_bubble_plot(input)
p

ply<-plotly::ggplotly(p)

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

ply